Length of report (approx. 15000 char (incl. spaces, incl. References list, excl. Code listing), 20000 char max)

Introduction

Mountainbiking is a popular sport in Switzerland (Fischer et al., 2021), that raises both environmental and policy issues (Pröbstl-Haider et al., 2018). In order to better understand mountainbiking, this project takes a closer look at the movment of downhill mountainbiking. and be able to talk about these issues on an objective level, it could be of interest to find out more about mountainbiking from a spatial use and movement based perspective.

Mountain biking can be practiced on different surfaces (on or off-road), there is a preference for natural areas (Zajc & Berzelak, 2016).

movement data of interest. describing movement in GIScience In this project, we aim at filtering mountainbiking movement patterns in crowdsourced movement data according to specific movement criteria which should only apply to mountianbikers.

In this project, we aim at defining criteria that indicate mountainbiking movement patterns, and try to provide information about the characteristics of mountainbiking.

what are characteristics of mountainbiking? -> downhill (not uphill), in vegetated areas / trails (not on streets), within a certain speed range.

In order to filter the mountainbiking pattern, we defined three criteria with which we should be able to have a unique identifier. Those three criteria consist of the speed with which the person is moving, the groundtype on which the trajectory is laying and furthermore the steepness of the trajectory, which needs to be directed downhill. Using just one or two of the criteria will always still allow for other activities. Such that a trajectory placed on grass and in forests could still also be a hiker, which also applies to the downhill movement. The Speed of a mountainbiker is hard to define, but it is also not a unique identifier for this movement pattern.

Research Question

In this project we will work on the following two research questions:

Data and Methods

Datasets

For the project, I used the movement trajectories that were created by Lisa Wahlen when going mountainbiking. The trajectories were recorded by using the GPS-tracking App posmo (Genossenschaft Posmo Schweiz, 2022) with a sampling rate of 10 seconds. We include 3 mountainbiking tours Lisa completed in the period from the 6th of May to the 18th of May 2023. The tours were recorded in Switzerland in the areas of Wiriehorn (2 tours) and Valbirse (1 tour).

For the assessment of the criteria that describe the movement pattern of mountain biking, we used the MOPUBE vector dataset providing the land cover types of the canton of Bern, using the version last updated on the 23.05.23 (Amt für Geoinformation des Kantons Bern, 2023). In addidtion, we worked with parts of the swiss digital terrain model swissALTI3D (Bundesamt für Landestopografie swisstopo, 2022). This elevation data is provided as raster in a resolution of 0.5 m in grids of 1 km2. We worked with extracts of the datasets, covering the spatial extent of the trajectories within the Canton of Bern.

We chose the tour Lisa made on the 18th of May in the Wiriehorn area (Wiriehorn 05-18) to define the characteristics of the biking movement pattern and tried to apply the criteria to the tour at Wiriehorn on the 7th of May (Wiriehorn 05-07) and the tour in Valbirse on the 6th of May for verification. The trajectories included the car ride from Solothurn to the biking location and back. A first visualisation of transport modes of the data revealed that the mountainbiking part of the trajectories were labelled by the posmo app as “Car”, “Bus”, “Other” - and sometimes almost correctly with “Bike”.

Wiriehorn trajectory of the 18th of May
Navigating to the south of the trajectory you’ll find the mountainbiking part.

Wiriehorn trajectory of the 7th of May
Navigating to the south of the trajectory you’ll find the mountainbiking part.

Valbirse trajectory of the 6th of May
Navigating to the northeast of the trajectory you’ll find the mountainbiking part.

Preprocessing

All datasets of the trajectories were first converted to a georeferenced feature. Temporal outliers recorded the night before the trip started were removed, as well as unnecessary or redundant information about Lisa’s user ID, the weekday, or the place name.

Preprocessing Bodenbedeckung? in ArcGIS 50m buffer on both sides of the trajectory line

Preprocessing swissALTI3D? polygon extract during acquisition.

Methods

Three relevant criteria for mountainbiking are speed, downhill movement and ground cover (Quelle). For all three criteria I assessed the values and ranges fitting the mountainbiking pattern of the Wiriehorn 05-18 tour. Then I applied the criteria to the Wiriehorn 05-06 and the Valbirse tour.

Speed and segmentation

For every fix on the trajectory I calculated the average speed within the window of four neighboring points, similar to the moving window method of Laube & Purves (2011). The euclidean distance to the two points before and the two points after the fix was calculated and divided by the time difference between the fix and the two points before and after, respectively. Fixes where the speed calculation could not be completed because either the distance or the time between two points was 0, were regarded as static. On the Wiriehorn 05-18 tour, 80 points were labelled as static, and used for segmentation of the trajectory. They were located in places on the trajectory where little movement or a change in transport mode is plausible (e.g. at the bottom and the top of the cable car allmiried, somewhere in the middle of the downhill trail, at train stations in Solothurn). The segments were used to identify parts of the trajectory where Lisa was mountainbiking. The relevant segments were selected based on visual assessment of the movement parameter profile (Dodge et al. 2009) of the average speed (Figure below). I assumed that short segments and segments with high velocities are not biking.

However, the selected segments included the movement of the cable car Lisa took to get uphill to the trail start. To isolate the cable car and determine the relevant speed range for mounainbiking, the average speed distribution of the selected segments was calculated. The speed values that best characterized the movement of mountain biking were identified through a process of testing.

Ground cover

The MOPUBE dataset showed 22 ground cover types (see figure below). Knowing about the preference for natural contexts for mountainbiking (Zajc & Berzelak, 2016) I assume that Lisa rode on vegetated and/or natural groundcovers. Thus, every groundcover type seems suitable except for “Abbau, Deponie”, Bahn”, “Gebäude”, “Strasse, Weg”, “Trottoir”, “übrige befestigte” and “Verkehrsinsel”. We could argue that water bodies are not suitable as well, but depending on their size it is not impossible to cross such features when biking.

To determine on which parts of the trajectory Lisa moved offroad, I intersected the ground cover dataset MOPUBE and the movement points. This reduced the trajectories to the spatial extent of the Canton of Bern, therefore excluding parts of the car ride from and to Solothurn. This step called track annotation (Dodge et al., 2013) allowed to add environmental information to the points on the trajectory. All points associated with natural ground cover were considered as matching the criteria and labelled accordingly.

Downhill movement

The elevation data was added to the dataset of the Wiriehorn 05-18 trajectory and the difference in elevation to the fourth point after the fix in the direction of movement was calculated. If within this 40 second window the difference in altidude was negative, it is assumed to be downhill movement.

Combination of criteria and refining steps

For every point on the trajectory I displayed how many of the three criteria applied. Based on the count of matching criteria, I isolated new segments that represent downhill mountainbiking according to the following conditions: The neighboring four fixes must show a sum of at least 6 matched criteria, and, within one Minute, there is at least one fix that matches all three of the criteria.

Limitations

The average Speed on the Wiriehorn 05-18 tour was 37.33548 km/h. The biking segments (including the cable car) showed an average speed of 7.80428 km/h. Since Lisa took the cable car several times on the Allmiried tour, the second and the third quartile of the speed distribution represent the speed of the cable car. In consequence, the first and the fourth quartile of the distribution were chosen to represent mountainbiking, resulting in a range of speed values from 0 to 2.855 and from 10.308 to 26.95490. These criteria also preserve the transport mode “walking”.

  • generalize
  • visually choosing relevant segments
  • quantyfying mountainbiking points, evaluation
  • cable car within segments. no segments only including biking
  • speed range chosen to exclude cable car.
  • verification /double check with lisa

Results

Wiriehorn 05-18

On the Wiriehorn 05-18 trajectory, the points where I assumed Lisa went mountainbiking generally show a higher count of criteria matched. The straight Line uphill represents the cable car, the sinuously distributed points represent the presumed biking pattern. Depending on the criteria, some points on the trajectory received a higher count.

Wiriehorn 05-18. Number of matched criteria for mountainbiking (mtb) for each point.

Wiriehorn 05-18. Speed (mtb_speed), groundcover (mtb_gc), downwards movement (mtb_elevation)

After applying the refining criteria, the basis for the new segmentation included all of the downhill biking trajectory except for 11 points.

Wiriehorn 05-18 trajectory. The points matching the refined criteria return TRUE.

The results of the second segmentation step represent the mountainbiking pattern.

Application to other movement trajectories

Wiriehorn 05-07

  • How can speed, ground cover and downward motion be used to characterize the movement pattern of downhill mountainbiking?
  • Can I detect segments where mountainbiking occurred based on speed, groundcover and downward motion?
  • Are the chosen criteria applicable to other movement trajectories?

On the Wiriehorn 05-07 trajectory, a higher count of criteria matched is visible where Lisa went mountainbiking. The count varies depending on the criteria.

Wiriehorn 05-07. Number of matched criteria for mountainbiking (mtb) for each point.

Wiriehorn 05-17. Speed (mtb_speed), groundcover (mtb_gc), downwards movement (mtb_elevation)

Wiriehorn 05-07 trajectory. The points matching the refined criteria return TRUE.

The results of the refined segmentation step capture the mountainbiking pattern.

Valbirse

On the Valbirse trajectory many points that are not part of the biking pattern fit more than one criteria.

Valbirse. Number of matched criteria for mountainbiking (mtb) for each point.

Taking a closer look, parts of the cable car did not match the criteria of downward movement (mtb_elevation)

Valbirse. Speed (mtb_speed), groundcover (mtb_gc), downwards movement (mtb_elevation)

Valbirse trajectory. The points matching the refined criteria return TRUE.

tm_shape(valbirse_try)+
  tm_dots("ID_mtb")+
  tm_view(set.view = c(7.30973, 47.15863, 11))

The res

The detection of the downhill movement pattern based on speed, groundcover and downward motion was successful for Wiriehorn 05-18 and Wiriehorn 05-07.

Discussion

characterization not applicable everywhere

track annotation with environmental data (Dodge et al., 2013) GlobCover (http://due.esrin.esa.int/page_globcover.php) is from 2009, MOPUBE is updated weekly.

known biking trails

References

APA 7th edition instructions: https://libguides.jcu.edu.au/apa/books#s-lg-box-21191622

Amt für Geoinformation des Kantons Bern. (2023). Amtliche Vermessung vereinfacht [Map]. 2023https://www.agi.dij.be.ch/de/start/geoportal/geodaten/detail.html?type=geoproduct&code=MOPUBE

Bundesamt für Landestopografie swisstopo. (2022). swissALTI3D: das hochaufgelöste Terrainmodell der Schweiz [Map]. https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html

Dodge, S., Weibel, R., & Forootan, E. (2009). Revealing the physics of movement: Com- paring the similarity of movement characteristics of different types of moving objects. Computers, Environment and Urban Systems, 33(6), 419–434. https://doi.org/10.1016/j.compenvurbsys.2009.07.008

Dodge, S., Bohrer, G., Weinzierl, R., Davidson, S. C., Kays, R., Douglas, D., Cruz, S., Han, J., Brandes, D. & Wikelski, M. (2013). The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data. Movement Ecology, 1(1), 3. https://doi.org/10.1186/2051-3933-1-3

Fischer, A., Lamprecht, M., & Bürgi, R. (2021). Mountainbiken in der Schweiz 2020: Auswertung Mountainbikeland-Befragung 2019 und Sekundäranalyse von «Sport Schweiz 2020». Bundesamt für Strassen ASTRA und Stiftung SchweizMobil. https://www.astra.admin.ch/astra/de/home/themen/langsamverkehr/materialien.html

Genossenschaft Posmo Schweiz. (2022). Posmo Project App - Tracking für Gruppen. https://posmo.coop/produkte/posmo-project-tracking-fuer-gruppen

Laube, P. (2014). Computational Movement Analysis. Springer Cham. https://doi.org/10.1007/978-3-319-10268-9

Laube, P. & Purves, R.(2011) How fast is a cow? Cross-scale Analysis of Movement Data, Transactions in GIS, 15(3), 401–418, John Wiley & Sons Ltd, DOI: 10.1111/j.1467-9671.2011.01256.x

Dodge, S., Laube, P., & Weibel, R. (2012). Movement similarity assessment using symbolic representation of trajectories. International Journal of Geographical Information Science, 26(9), 1563-1588. https://doi.org/10.1080/13658816.2011.630003

Pröbstl-Haider, U., Lund-Durlacher, D., Antonschmidt, H. & Hödl, C. (2018). Mountain bike tourism in Austria and the Alpine region – towards a sustainable model for multi-stakeholder product development. Journal of Sustainable Tourism, 26(4), 567-582. https://doi.org/10.1080/09669582.2017.1361428

Zajc, P. & Berzelak, N. (2016). Riding styles and characteristics of rides among Slovenian mountain bikers and management challenges. Journal of Outdoor Recreation and Tourism, 15, 10-19. https://doi.org/10.1016/j.jort.2016.04.009